CN113811929A - Combined control of vehicles travelling on different intersecting roads - Google Patents

Abstract

A system for jointly controlling a vehicle based on traffic configuration of an intersection area of a first road and a second road. The intersection area includes a control area and a ranking area covering a section of the first road and the second road near the intersection. The system groups vehicles traveling on the first and second roads in the sequencing area into a series of vehicle groups and prevents different groups of vehicles from traveling simultaneously in the control area so that a first vehicle of a subsequent group cannot pass over a last vehicle of a previous group. The system determines a movement trajectory of the same group of vehicles traveling within the control area on the first road and the second road through the intersection, and transmits the movement trajectory to the corresponding vehicle.

Description

Combined control of vehicles travelling on different intersecting roads

Technical Field

The present invention relates generally to vehicle control, and more particularly, to a method and apparatus for jointly controlling vehicles traveling on different roads.

Background

Managing the effective traffic flow on national roads is an extremely complex problem. This problem is further complicated by the fact that roads have reached or even exceeded their capacity in several metropolitan areas. One particularly difficult area to manage is the merging of two or more lanes into one lane and/or the intersection of different lanes and roads. Various reasons for this include: road maintenance/construction requiring unrestricted access to the lanes, requiring closure of the lanes, or road designs requiring merging of one lane with another, a feature very common in road entry ramps.

The internet of vehicles (IoV) is a necessary convergence of the mobile internet and the internet of things (IoT). IoV enable information collection, information sharing, and information processing to efficiently guide and supervise the vehicle. IoV differ from IoT due to mobility, security, V2X communication, power savings, security attacks, etc. At IoV, information and communication techniques are applied to the infrastructure, vehicles, and users to manage traffic and vehicle mobility. IoV are intended to provide innovative services and controls for traffic management and to enable users to better understand situations and to enable safer, more coordinated, and more intelligent use of traffic networks.

More and more networked vehicles and autonomous vehicles are emerging. Mobility control of these types of vehicles is based not only on actions of the driver, but also on advanced control techniques using communication, sensors, optimization control techniques, and the like. Unlike conventional control mechanisms such as traffic lights and stop signs, advanced control mechanisms can optimize control efficiency by making control decisions optimized in real time. Therefore, there is a need to extend the control method to joint control of vehicles traveling on intersecting roads so that the vehicles do not need to stop in order to pass through intersections or road merge points. Therefore, it is necessary to control the vehicle traveling on the cross road.

The development of autonomous vehicles is emerging. Therefore, on-board control (on-board control) in which a vehicle uses communication data, advanced devices such as radar, lidar, a camera, GPS, etc., and Artificial Intelligence (AI) technology to automatically control the mobility of the vehicle has received much attention. On-board control has its advantages in knowing vehicle characteristics (e.g., engine, steering wheel, braking conditions, vehicle model, vehicle size, etc.). The onboard controls may enable dynamic vehicle parameter configuration. However, the vehicle-mounted control is insufficient in information resources and communication capacity, and cannot make an optimal action decision. The in-vehicle control device cannot obtain information about neighboring vehicles, pedestrians, and environmental conditions outside its visible range.

There are several approaches that can address the problems inherent in merge or intersection areas through cooperative communication between different vehicles. For example, there are several systems that employ smart cars or smart cars. The basic premise of all these systems is that vehicles may be equipped with means that allow them to communicate with each other on the road in their vicinity. Such vehicles are able to operate in a cooperative manner by communicating with each other, allowing for maximum safe throughput (throughput) of vehicles on the road. Examples of this type of system are disclosed in U.S. patent application nos. 2004/0260455, 2004/0068393, and 2005/0137783. However, communicating between these vehicles to ensure optimization and safety of vehicle control remains a challenging problem.

One drawback of these systems is that they require that all vehicles be equipped with special equipment. Not only does this take years to achieve, but old vehicles may not be economically retrofitted. In particular, different communication technologies have been developed to support vehicle communication. For example, the IEEE has developed a family of wireless access (DSRC/WAVE) standards in dedicated short-range communication/vehicular environments for vehicular networks. The 3GPP has added new ad hoc features to the cellular ecosystem to enable short-range communication in the so-called cellular vehicle-associated universe (C-V2X). For cost reasons, it is impractical for a vehicle to install more than one short-range communication technology. Thus, a vehicle equipped with DSRC/WAVE cannot communicate with a vehicle equipped with C-V2X, and vice versa. Information shortages and communication capacity constraints may severely limit the accuracy of vehicle mobility control decisions made by on-board control devices.

An alternative method of communication between vehicles is through the so-called "cloud" (i.e., through a remote server). The cloud model finds wide application in IoT. The cloud model has advantages in data storage, data processing capability, and information sharing such as multimedia, maps. However, the cloud model has a disadvantage in performing real-time vehicle mobility control due to a long delay caused by multi-hop communication. In addition, the cloud does not have immediate information of vehicles, pedestrians, and environments such as road conditions and weather conditions that are critical to controlling vehicle mobility (particularly autonomous vehicles).

Therefore, there remains a need to provide a method of controlling vehicles near merge points and intersections.

Disclosure of Invention

It is an object of some embodiments to provide a system and method for joint control of vehicles travelling on intersecting roads. Additionally or alternatively, another goal of some embodiments is to optimize control efficiency by making real-time optimal control decisions such that the vehicle does not need to stop to pass through intersections and/or highway mergers.

Some embodiments are based on the recognition of the complexity of such joint control problems. For example, one of the problems addressed by some embodiments is the arrangement of control systems for real-time joint control configurations. For example, some embodiments are based on the following recognition: cloud control is impractical for optimally controlling intersection traffic and/or highway merging. Due to multi-hop communication delays, cloud control may not be able to meet the real-time constraints of security requirements. Additionally, the cloud does not have immediate information of vehicles, pedestrians, road conditions to make optimal decisions. On the other hand, the on-board controls may not have enough information to make optimal decisions, e.g., the on-board controls do not have information about the movement of objects outside the visible range, nor receive information from vehicles outside the communication range. Additionally, in-vehicle control may also present communication limitations (e.g., a vehicle equipped with an IEEE DSRC/WAVE radio may not be able to communicate with a vehicle equipped with a 3GPP C-V2X radio) due to the presence of heterogeneous in-vehicle communication technologies such as Wireless Access in IEEE short-range communication/in-vehicle environments (DSRC/WAVE) and 3GPP cellular Universal for vehicle (C-V2X).

To this end, some embodiments are based on the following recognition: due to its unique characteristics such as direct communication capability with the vehicle, knowledge of road conditions, and environmental point of view via cameras and sensors, edge devices such as Road Side Units (RSUs) are viable control points for making optimal decisions for real-time control of intersection traffic or highway merging. In addition, edge devices at control points, such as intersections or highway mergers, can make joint control decisions via real-time collaboration and information sharing. To this end, some embodiments implement real-time edge control using edge devices.

Advanced vehicle mobility control is accomplished by operating the control method using effective communication data and sensor data. The vehicle environment is a highly dynamic environment. In addition to vehicle dynamics, there are unpredictable environmental dynamics, e.g., random motion of objects such as pedestrians and animals, emergencies caused by trees and infrastructure. Therefore, the control method needs to adapt quickly to the overall environmental dynamics. The operating time of the control method depends on the number of vehicles, the complexity of the control technology, the resources of the control device, etc. To be safe, the computation run time needs to be below a threshold. For a control device, it is impractical to dynamically update its control technology and computational resources. However, it is feasible to adjust the number of vehicles involved in each operation control technique.

Some embodiments are based on the following recognition: it is desirable to provide optimal control of vehicles traveling on intersecting roads, for example, without stopping the vehicle to let other vehicles pass. Some embodiments are based on the following recognition: in general, various motion planning methods can be utilized to solve such optimization tasks. However, when the number of vehicles approaching the control point is too large, the optimization problem may be too complex to solve online or to avoid violating safety constraints.

To this end, some embodiments are based on the following recognition: in order to ensure the safety of the road intersection traffic or the highway merger, the number and/or state of vehicles approaching the control point should be restricted (i.e., limited). To this end, some embodiments divide the road segments proximate to the control point into at least a sequencing region and a control region. In the sequencing zone, the vehicles are divided into groups (clusters), and different groups of vehicles are prevented from traveling within the control zone at the same time, so that a first vehicle of a subsequent group (cluster) cannot pass over a last vehicle of a previous group (cluster). In this way, the number of vehicles in the control area is limited. When a vehicle enters a control area, embodiments determine a motion trajectory and motion strategy for those vehicles in the same group. Embodiments ensure safety and optimality of passing intersections because the number of vehicles within the control area is limited.

To this end, some embodiments divide the road proximate the control point into a rank region and a control region based on vehicle state, road conditions, and road geometry. Each zone includes segments of multiple roads over which vehicles are moving toward control points such as intersections and highway merge points. For each road, the control region is a link between the control point and the end of the sequencing region. The goal of the joint control for the sequencing zones is to select the number of vehicles for the control zone so that the control method can generate safe trajectories for vehicles within the control zone to pass through the intersection or merge into the highway. The goal of control in the control area is to generate a safe motion trajectory that can optionally optimize performance metrics. The road segments of different roads may differ in size. The division may be performed by various methods. As used herein, the motion profile of a vehicle defines the position of the vehicle as a function of time.

For example, in one embodiment, the zones are divided based on traffic speed such that the higher the traffic speed, the larger the zone. Additionally or alternatively, in one embodiment, the zones are partitioned based on traffic density speed such that the higher the density, the smaller the zone. Additionally or alternatively, in one embodiment, the regions are divided based on the number of lanes of the road such that the more lanes, the smaller the region. Additionally or alternatively, in one embodiment, the regions are partitioned based on the number of intersecting roads such that the more roads, the smaller the region. Additionally or alternatively, in one embodiment, the zone division is based on a combination of traffic speed, traffic density, number of lanes, and number of roads.

Some embodiments are based on the following recognition: the edge calculation may pre-calculate some information to group vehicles within the rank region. Before a vehicle enters the rank region, information including vehicle status, road maps, road conditions, maximum number of vehicles allowed within the control region, and the like may be calculated.

To this end, some embodiments are based on the following recognition: the edge calculation stores the road map and the maximum number of vehicles allowed in the control area in memory, and dynamically collects vehicle states and road conditions such as pedestrian information by the cameras and sensors of the edge device. Before the vehicle enters the sequencing zone, the edge calculation communicates with the vehicle to obtain its status.

Some embodiments are based on the following recognition: the grouping of vehicles in the sequencing zone is to prevent different groups (clusters) of vehicles from traveling into the control zone at the same time, adding control complexity and computational error so that the first vehicle of a subsequent group (cluster) cannot pass the last vehicle of a previous group (cluster). The vehicles in a group (cluster) may include vehicles moving from different intersection roads to a control point such as a road intersection or a road merge point.

To this end, some embodiments are based on the following recognition: the vehicle grouping is based on an estimated time of arrival of the vehicle at the control area. In the rank region, the edge calculation calculates an estimated arrival time of each vehicle based on the current state of each vehicle and the road map. Vehicles with similar arrival times are grouped into the same group (cluster). For each group (cluster), the edge computation computes the optimal arrival time by solving a mixed integer linear programming problem and determines a velocity constraint to ensure that the groups (clusters) arrive at the control region in turn. In effect, the calculation allows the vehicle to pass through the intersection without stopping. Different groups (clusters) of vehicles are prevented from entering the control area simultaneously so that the first vehicle of a subsequent group (cluster) cannot pass the last vehicle of a preceding group (cluster). In fact, the clustering of vehicles in different groups imposes constraints on the computational complexity of the motion trajectory determination problem, allowing the motion scheduling problem to be solved in real time. The optimal arrival time, speed constraints and/or motion trajectory are sent to the corresponding vehicle.

Some embodiments are based on the following recognition: based on the motion trajectory and the motion strategy determined by the control technique, the vehicles within the control area are controlled to travel through the control area in an optimal manner.

To this end, some embodiments are based on the following recognition: edge calculations control the vehicles within the control area to optimize certain performance metrics (e.g., minimize total fuel consumption). The edge calculation divides a time interval from a time when the vehicle enters the control area to a time when the vehicle reaches the control point into a plurality of time steps, and calculates a motion trajectory and a control strategy of each vehicle at each time step by using the optimal arrival time, the vehicle state, and the road map. At each time step, the control strategy includes position, velocity, acceleration, etc. In some implementations, the motion profile is determined to minimize total fuel consumption or to minimize total air pollutant emissions. The motion trajectory and the control strategy can be obtained by solving a constrained shortest path problem or a quadratic programming problem.

Accordingly, one embodiment discloses a system for jointly controlling a vehicle, the system comprising: a memory configured to store a traffic configuration of an intersection area of a first road and a second road, wherein the intersection area includes a sequencing area and a control area, wherein the sequencing area includes a link of the first road and a link of the second road near the intersection, and wherein the control area includes a link of the first road and a link of the second road between the intersection and a corresponding link of the sequencing area; a processor configured to: grouping vehicles traveling on the first road and the second road within the sorted area into a series of vehicle groups; preventing vehicles of different groups from entering the control area simultaneously so that a first vehicle of a subsequent group cannot pass over a last vehicle of a preceding group; determining the motion trail of the same group of vehicles running in the control areas on the first road and the second road to pass through the intersection; and transmitting the motion profile to the corresponding vehicle.

Another embodiment discloses a method for jointly controlling a vehicle, wherein the method uses a processor coupled to a memory storing a traffic configuration for an intersection area of a first road and a second road, wherein the intersection area comprises a sequencing area and a control area, wherein the sequencing area comprises segments of the first road and segments of the second road near the intersection, and wherein the control area comprises segments of the first road and segments of the second road between the intersection and corresponding segments of the sequencing area, wherein the processor is coupled with stored instructions implementing the method, wherein the instructions, when executed by the processor, perform steps of the method, the method comprising the steps of: grouping vehicles traveling on the first road and the second road within the sorted area into a series of vehicle groups; preventing vehicles of different groups from simultaneously traveling into the control area such that a first vehicle of a subsequent group cannot pass over a last vehicle of a preceding group; determining the motion trail of the same group of vehicles running in the control areas on the first road and the second road to pass through the intersection; and causing vehicles within the control area to follow the corresponding trajectory.

Yet another embodiment discloses a non-transitory computer readable storage medium having embodied thereon a program executable by a processor to perform a method. The method comprises the following steps: accessing a traffic configuration for an intersection area of a first road and a second road, wherein the intersection area comprises a sequencing area and a control area, wherein the sequencing area comprises segments of the first road and segments of the second road near the intersection, and wherein the control area comprises segments of the first road and segments of the second road between the intersection and corresponding segments of the sequencing area; grouping vehicles traveling on the first road and the second road within the sorted area into a series of vehicle groups; preventing vehicles of different groups from simultaneously traveling into the control area such that a first vehicle of a subsequent group cannot pass over a last vehicle of a preceding group; determining the motion trail of the same group of vehicles running in the control areas on the first road and the second road to pass through the intersection; and causing vehicles within the control area to follow the corresponding trajectory.

Drawings

[ FIG. 1]

FIG. 1 shows a traffic scenario illustrating some of the issues addressed by some embodiments.

[ FIG. 2]

FIG. 2 shows a schematic diagram illustrating area division near a road intersection, according to some embodiments.

[ FIG. 3]

FIG. 3 illustrates a block diagram of a method of determining a maximum number of vehicles in a control area to make optimal control decisions, according to some embodiments.

[ FIG. 4]

FIG. 4 illustrates a functional block diagram of a method of performing joint vehicle control at a control point, according to some embodiments.

[ FIG. 5]

FIG. 5 illustrates an example schematic diagram of a vehicle grouping for highway consolidation according to one embodiment.

[ FIG. 6]

FIG. 6 shows a snapshot of the same set of vehicles in different areas for a highway merge, where IoV-the edge is located at the merge point, according to one embodiment.

[ FIG. 7]

Fig. 7 illustrates an example of a control or motion profile determined by some embodiments.

[ FIG. 8]

FIG. 8 illustrates a schematic diagram of a graph-based vehicle motion trajectory planning method, according to some embodiments.

[ FIG. 9]

FIG. 9 illustrates a block diagram of a control system for jointly controlling a vehicle, according to some embodiments.

[ FIG. 10A ]

FIG. 10A shows a schematic diagram of a vehicle including a vehicle controller in communication with the control system of FIG. 9.

[ FIG. 10B ]

FIG. 10B illustrates a schematic diagram of interactions between different controllers of a vehicle, according to some embodiments.

[ FIG. 10C ]

FIG. 10C shows a schematic diagram of an autonomously or semi-autonomously controlled vehicle whose dynamically feasible and generally optimal trajectory may be calculated using some embodiments.

[ FIG. 11]

FIG. 11 illustrates a block diagram of various components that may be used to implement joint control using alternative computers or hardware processors, according to some embodiments.

Detailed Description

FIG. 1 shows a traffic scenario illustrating some of the issues addressed by some embodiments. Some embodiments are based on the following recognition: in order to establish communication between different vehicles on a road in the IoV environment, information transfer between the cloud and the vehicle needs to pass through a core infrastructure network and a Road Side Unit (RSU) as shown in fig. 1, and communication between the vehicle V1110 and the cloud 120 needs to pass through the RSU 130 and the core network 140 in fig. 1. However, the multi-hop long latency makes cloud-based vehicle control methods impractical for real-time control and service.

In addition, the in-vehicle control device cannot obtain information about neighboring vehicles, pedestrians, and environmental conditions outside its visible range. Fig. 1 shows an example in which the vehicle V2150 is intended to pass through an intersection once the large vehicle V3160 passes through the intersection. However, the small vehicle V4170 is driving into the intersection and the large vehicle V3 prevents the vehicle V2 from seeing the small vehicle V4. Therefore, V2 and V4 may collide because the communication link between V2 and V4 may also be blocked.

In addition, on-board controls may also have communication limitations. Different communication technologies have been developed to support vehicle communications. The IEEE has developed a family of wireless access (DSRC/WAVE) standards in dedicated short-range communication/vehicular environments for vehicular networks. The 3GPP adds new dedicated functions to the cellular ecosystem to enable short-range communication in the so-called cellular vehicle-associated universe (C-V2X). For cost reasons, it is impractical for a vehicle to install more than one short-range communication technology. Thus, a vehicle equipped with DSRC/WAVE cannot communicate with a vehicle equipped with C-V2X, and vice versa. Information shortages and communication capacity constraints can severely limit the accuracy of vehicle mobility control decisions made by on-board control devices.

Some embodiments are based on the following recognition: compared to remote cloud and on-board devices, IoV-edge (IoV-edge) devices (e.g., RSUs) have advantages in real-time vehicle mobility control: (1) an IoV-edge device installed at an intersection or highway merge point can communicate directly with vehicles approaching the intersection or merge point; (2) IoV-the edge device may be equipped with a variety of communication technologies and therefore may communicate with all vehicles; (3) IoV-the edge devices can enable real-time collaboration of vehicle status and environmental viewpoints via a robust high-speed communication link; (4) IoV-the edge device is stationary, which makes IoV-the communication between the edge device and the vehicle more reliable and the collected environmental data is of higher quality; and (5) IoV-the edge device can continuously monitor vehicle traffic and the environment to make accurate decisions. All of these characteristics make the IoV-edge device a suitable point for making optimal vehicle mobility control decisions.

Control point selection is critical to performing real-time vehicle mobility control. Due to multi-hop communication delays, cloud control cannot meet the real-time constraints of security requirements. Additionally, the cloud does not have immediate information of vehicles, pedestrians, and road conditions to make optimal decisions. The on-board control does not have comprehensive information to make optimal decisions. In addition, in-vehicle control may also have communication limitations due to the existence of heterogeneous in-vehicle communication technologies such as IEEE DSRC/WAVE and 3GPP C-V2X. Edge devices such as RSUs are feasible points for making optimal decisions for real-time vehicle control due to their direct communication capabilities, road condition knowledge, environmental viewpoints and real-time collaboration capabilities.

Accordingly, some embodiments perform edge calculations with edge devices for achieving real-time optimal vehicle control of intersections and highway merge points. In this disclosure, an edge device such as an RSU or eNodeB is referred to as an IoV-edge.

FIG. 2 shows a schematic diagram illustrating area division near a road intersection, according to some embodiments. In order to make safe and optimal control decisions, IoV-edges installed at control points such as intersections or highway mergers divide roads within a communication range into three regions, as shown in fig. 2, which illustrates a bidirectional intersection region division in which east (east bound) roads intersect north roads, as shown in fig. 2. IoV-edge 210 is located at the intersection. IoV-the communication range between the edge and the vehicle is R220. The links within the communication range are divided into an information area 230, a ranking area 240, and a control area 250.

In the information area 230, IoV-the edge communicates with the vehicle and collects vehicle information such as ID, position, speed, acceleration, lane information, intention at an intersection, etc. In the sequencing area 240, IoV-the edges calculate estimated arrival times to the control area based on vehicle status and road maps, divide the vehicles into groups (clusters), determine the optimal time to reach the control point for each vehicle, and calculate the speed limit for each vehicle to keep the clusters entering the control area in turn. A control point is a geometric location of a road intersection (e.g., a highway merge point). IoV-edge determines a trajectory of motion within the control area for each vehicle based on optimal arrival time, vehicle position, vehicle speed, vehicle acceleration, speed limits, acceleration limits, headway constraints, road maps, and the like. The motion trajectory is expressed in terms of vehicle position, vehicle speed and vehicle acceleration at different time steps. IoV-edge controls vehicle mobility based on motion trajectory.

The vehicle environment is a highly dynamic environment. In addition to vehicle dynamics, there are unpredictable environmental dynamics, e.g., random motion of objects such as pedestrians and animals, and emergencies caused by trees and infrastructure. Therefore, the control method needs to adapt quickly to the overall environmental dynamics. In other words, the control method must be fast enough to reflect the dynamics of the vehicle environment. The operating time of the control method depends on the number of vehicles involved, the complexity of the control technology, the resources of the control device, etc. To be safe, the computation time needs to be below a threshold. IoV-the edges are equipped with appropriate control techniques and computational resources. For the IoV-edge, it is impractical to dynamically update its control techniques and computational resources. However, it is feasible to adjust the number of involved vehicles to reduce the operating time of the control technique.

FIG. 3 illustrates a block diagram of a method of determining a maximum number of vehicles in a control area to make optimal control decisions, according to some embodiments. In some implementations, the method is performed off-line (offline). Initially, IoV-the edges may select a sufficient number of vehicles 300. Given the control technology, computing resources, and road map information at the control point, IoV-the edge uses the control algorithm 305, the initial number of vehicles 300 as the number of vehicles 310, the computing resources 315, the communication data 320, and the sensor data 325 as inputs to calculate the runtime 330 of the control technology. IoV-the edge, on the other hand, calculates a control command transmission frequency 345 based on the safety requirements 335 and the road map 340. The edge then checks IoV whether the runtime can satisfy the security constraints 350. If not, IoV-the edge reduces the number of vehicles 355 and repeats the process. The reduced number of vehicles is then used as the number of vehicles for recalculation 310. If so, the number of incoming vehicles 310 is the number of vehicles possible in the control area 360. Once the maximum number of vehicles is determined IoV-the edges may determine the area size 370 using the vehicle status 365 and the map information 375.

In various embodiments, the number of vehicles in a group (cluster) is less than the maximum number of vehicles allowed in the control area. Once the maximum number of vehicles allowed in the control area is determined, the zone division may be performed based on different metrics (metrics). For example, the zone division may be based on traffic speed, such that the higher the traffic speed, the larger the zone; the zone division may be based on traffic density, such that the higher the density, the smaller the zone; the zone division may be based on the number of lanes of the road, such that the more lanes there are, the smaller the zone; the zone division may be based on the number of intersecting roads, such that the more roads, the smaller the zone; and the zone division may be based on a combination of traffic speed, traffic density, number of lanes, and number of roads. Since traffic conditions and road conditions vary from road to road, the size of the area also varies from road to road. The following is a method for region division.

At the control point, let N_c ^maxIs the maximum number of vehicles allowed in the control area. Suppose a vehicle is driven from N_RStripe road R₁、R₂、……、R_NRClose to the control point. On the road R_i(i＝1、2、…、N_R) Upper, the density of the vehicle in the direction approaching the control point is D_i. Is provided with Z_iTo aim at road R_iThe size of the control region of (1). Total number of vehicles N in control area_cIs given by

Region division to select proper Z_iSo that N is_c≤N_c ^max. Since the speeds on different roads areDifferent. Therefore, the control region division is expressed as the following linear optimization problem

Subject to the following constraints

Z_i>0 (4)

If it is not

Then

Wherein, V_RiAnd V_RjAre respectively a road R_iAnd road R_jAbove, average speed. The constraint (3) ensures that the number of vehicles within the control area does not exceed a threshold. Constraint (4) indicates that all relevant roads must be considered. The constraint (5) takes into account the vehicle speed. The higher the speed, the larger the zone size. This is because the distance headway (distance headway) is greater on higher speed roads.

The information area is determined so that IoV-the edge can obtain the necessary information from the vehicle. Suppose a vehicle per T_bThe time periods broadcast their status once, for example, every 100 milliseconds the vehicles broadcast their status using IEEE DSRC/WAVE. Thus, for the road R_iHas an information area length of V_Ri*T_b. Using this length, the information area can be determined.

In some embodiments, once both the control region and the information region for the road are determined, the remaining portion within the communication range is the rank region. Additionally or alternatively, the remainder of the communication range may be divided over the sequencing region and the information region. In the information area, some embodiments collect information about vehicles for grouping. The information zone precedes the rank zone, and some embodiments determine a status of vehicles traveling within the information zone and track the status of vehicles within the information zone to estimate a current status of vehicles within the rank zone. In some implementations, the information region is implemented as part of the sorting region.

FIG. 4 illustrates a functional block diagram of a method of performing joint vehicle control at a control point, according to some embodiments. After the area division is completed, IoV-edge performs the operation shown in fig. 4 for joint control, where IoV-edge calculates estimated arrival time 410 to the control area for each vehicle in the ranked area based on the vehicle state and the road map. Then, IoV-the edges divide the vehicles in the sorted area into groups (clusters) 420 based on the estimated arrival times. Then, IoV-edge calculates the optimal arrival time to the control point and the speed constraint 430 for each vehicle by applying the Mixed-Integer Linear Programming problem (Mixed-Integer Linear Programming project) described later. The speed constraint is to ensure that groups of vehicles (clusters of vehicles) enter the control area in sequence and is different from the speed limit set by traffic regulations. Next, IoV-edge sends the optimal time of arrival and speed constraints 440 to the vehicle. Before the vehicles enter the control area, IoV-the edges determine the vehicle motion profile 450 for each vehicle and send the motion profile to the vehicle 460. Once the vehicle enters the control area, IoV-the edge may continually update the motion profile 470 to accommodate the change in the instant message. If necessary, the updated trajectory is sent to the vehicle.

FIG. 5 illustrates an example schematic diagram of a vehicle grouping for highway consolidation according to one embodiment. In this example, the road segments and the on-ramp segments are divided into an information region 230, a sequencing region 240, and a control region 250. IoV-edge 210 is located at merge point 510 (i.e., the control point). The vehicles in the sequencing zone are divided into three clusters: group 520, group 530, and group 540.

Fig. 6 shows a snapshot of the same set of vehicles merged in different areas for a highway where IoV-edge 210 is located at the merge point (i.e., control point), according to one embodiment. The vehicle group 610 enters the information area and communicates 620 with IoV-edge 210 for their status. The vehicle group 630 then enters a sequencing zone where the optimal merge time, speed constraints and motion trajectory are sent to the vehicle 640. The vehicle then moves to a control area, where the vehicle moves according to a motion profile. It can be seen that vehicles v1 and v3 accelerate to accommodate the merge 650 of vehicles v5 and v 6. Finally, the merge is complete and the vehicle passes through the merge point 660 in the order v3, v6, v1, and v 5.

There are a number of ways to calculate the estimated time of arrival, for example, by using the speed at which the vehicle enters the sequencing zone and the distance to the access point. Let D_RFor the distance on the road R from the starting point of the sorting area to the starting point of the control area, v_iThe speed of the vehicle i, the estimated arrival time of the vehicle i is D_R/v_i。

The speed constraint is estimated such that for two consecutive groups (clusters) on the same road, the time at which the last vehicle of the first group (cluster) enters the control area is earlier than the time at which the first vehicle of the second group (cluster) enters the control area. Let t_i ⁰(I ═ 1,2, …, I) is the time at which a vehicle in the first group (cluster) enters the sequencing zone, and v is_iIs the corresponding speed of the vehicles in the first group (cluster); and t_j ⁰(J ═ 1,2, …, J) is the time at which a vehicle in the second group (cluster) enters the sequencing region and v_jIs the corresponding speed of the vehicles in the second group (cluster), then the time at which the last vehicle of the first group (cluster) enters the control area is denoted by t_last＝max{t_i ⁰+D_R/v_iGiven by t, and the time at which the first vehicle of the second group (cluster) enters the control area is given by t_first＝min{t_j ⁰+D_R/v_jGiving. Constraint t_last<t_firstGiven that, for any vehicle i in the first group (cluster), its speed satisfies v_i>D_R/(t_first–t_i ⁰) And for any vehicle j in the second group (cluster), its speed satisfies v_j<D_R/(t_last-t_j ⁰)。

In some embodiments, calculating the optimal arrival time is expressed as a Mixed Integer Linear Programming (MILP) problem. For a vehicle with ID ═ i, its state includes vehicle position, vehicle speed, distance to control point, lane number, time the vehicle entered the sequencing zone, and earliest possible time the vehicle reached the control point, i.e., { x ═ i_i,v_i,d_i,l_i,t_i ⁰,t^min _i}. Suppose that the vehicle follows a path having a maximum allowable speed v_maxSpecified speed rule (i.e., v)_i≤v_max). Then, t^min _iFrom t^min _i＝v_i+a_max*Δt_iIs given in which a_maxIs the maximum allowable acceleration, Δ t_iIs that the vehicle i is accelerated at the maximum acceleration a_maxIts speed is changed from v_iIncrease to v_maxThe time required. For optimization problems, the decision variable is the optimal arrival time t^opi (i ═ 1,2, …, N), where N is the vehicle number in the group (cluster). The objective function is defined as

And the optimization problem is defined as

Subject to the following constraints

Wherein l_i＝l_j (9)

Wherein, t_headwayIs the time headway between adjacent vehicles on a road, and may be different for different roads. It can be seen that the goal of the MILP problem (7) is to minimize the total travel time of all vehicles in a group (cluster) through the control area. Constraints (8) ensure that the vehicle passing through the control area does not violate speed limits. The other two constraints (9) ensure that a safe separation of vehicles on the same lane is maintained. Solving the optimization problem given by (6) - (9) gives the optimal arrival time for the vehicles in a group (cluster). There are a variety of optimization problem solvers that can be used to solve the formulation problem (e.g., the IBM CPLEX solver).

After obtaining the optimal arrival time, the task is to determine a motion trajectory that can be expressed in terms of (time, position, velocity, acceleration).

Fig. 7 illustrates an example of a control or motion profile determined by some embodiments. The motion profile defines the position of the vehicle as a function of time. For example, the motion profile specifies one or a combination of the position, velocity, acceleration of the vehicle at each time instance. In this example, the trajectory is defined at time t₁At the point 710, the process is completed,

at time t₂At the location of the 720-point location,

and finally at time t_mAt the location of the location 730,

the goal of trajectory planning is to minimize a cost function (cost function) such as vehicle energy consumption or air pollutant emissions through a control area while following a specified optimal arrival time to an access point.

It has been shown that vehicle energy consumption is highly dependent on vehicle type, acceleration/deceleration, speed profile, road gradient, etc. The optimization problem becomes a constrained nonlinear programming problem. Solving this constrained nonlinear programming problem is very challenging. Non-convexity and high non-linearity typically result in significant computational cost and are hardly feasible for real-time microscopic vehicle control.

Different embodiments use different methods to calculate the optimal vehicle trajectory at IoV-edge. For example, one embodiment uses a graph-based optimal trajectory planning method.

FIG. 8 illustrates a schematic diagram of a graph-based vehicle motion trajectory planning method, according to some embodiments. To optimize energy consumption, a graph-based optimal trajectory planning method with constraints on safe vehicle distance, maximum speed, maximum possible acceleration/deceleration rate, etc. is provided. To represent this graph-based problem, time, position, and velocity are discretized. Each node of the directed graph is represented as (t)_i,x_i,v_i)。

The state transitions are illustrated in FIG. 8, where for simplicity, the slave time t is used₀The distance to start driving replaces the position and the speed is used instead of the velocity. Planning calculation from time t₀Beginning at 810, the time step is Δ t 820. At time t₀Where the distance traveled equals zero. In the figure, the source node is (t)₀,0,v_i ^t0)830, and the destination node is (t)₄,d_i,v_i ^t4)840. At each time step, the current node and constraints from speed limits, acceleration limits, maximum power, and brake system capability are used to determine the feasible node for the next time step. For slave node (t)_i,x_i,v_i) To node (t)_i+1,x_i+1,v_i+1) Edge transition (edge transition) of (1), there is a cost as energy consumption during this state transition process. Thus, there may be many paths from the source node 830 to the destination node 840. Thus, the graph-based optimal trajectory planning problem translates into the problem of finding a path that minimizes the total energy consumption of all vehicles of a group (cluster). In FIG. 8, path 850 is an optimal path for the vehicle based on energy consumption.

Suppose for a carPath P of vehicle i_iBy M_iAn edge e^m _i(m＝1,2,…,M_i) Make up and aim at edge e^m _iCorresponding energy cost of c^m _i. For a vehicle in a group (cluster), the total energy cost is given by

Then, the optimal trajectory planning based on the graph is to find a path P for each vehicle i (i ═ 1,2, …, N)^o _iSo as to make

Wherein, P_o＝(P^o ₁,P^o ₂,……,P^o _N) And P ═ P_i,P₂,……,P_N)。

There are existing methods that can be applied to find shortest paths in directed graphs, such as Dijkstra's shortest path algorithm.

Optimal trajectory planning methods based on Quadratic programming (Quadratic programming based) can be used to maximize comfort and minimize air pollutant emissions.

Embodiments express the problem as L making the control input²Norm (i.e. acceleration/deceleration rate) minimization to provide a feasible trajectory based on quadratic programming techniques and to optimize overall vehicle mobility benefits such as reduction of air pollutant emissions.

The vehicle dynamics equation is as follows:

the quadratic programming problem using a convex objective function can be represented by a positive definite matrix H

Subject to the following constraints

0≤v_i(t_k)≤v_max...............(15)

a_min≤a_i(h_k)≤a_max............(16)

|x_i(t_k)-x_j(t_k)|≥d_headway.......(17)

Wherein the acceleration a_iIs a control input, H is a positive definite matrix such as an identity matrix, d_iIs the current distance to the control point, Δ t is the time step, x_i ⁰And v_i ⁰Are respectively the time t₀Position and velocity of (d)_headwayIs the safe distance between adjacent vehicles.

Solving the optimization problem given by (13) - (18) gives the optimal acceleration rate a for the vehicle i_i. A is to_iSubstituting into (12) to obtain v_iAnd x_i. As a result, a trajectory (t) for the vehicle i is obtained_i,x_i,v_i,a_i)。

FIG. 9 illustrates a block diagram of a control system 900 for jointly controlling a vehicle, according to some embodiments. The control system 900 is disposed near IoV-edges of control points such as merges and/or intersections of roads. This arrangement shows that the edge device includes or is operatively connected to a set of sensors to collect traffic information in the intersection area and send corresponding trajectories to vehicles in the control area.

The control system 900 may have multiple interfaces that connect the system 900 with other systems and devices. For example, a Network Interface Controller (NIC)950 is adapted to connect the system 900 via the bus 906 to a network 990 that connects the control system 900 with the devices 918 of the vehicle network. Examples of such devices include vehicles, traffic lights, traffic sensors, and the like. For example, the control system 900 includes a transmitter interface 960 configured to command the device 918 to move in a prescribed manner using a transmitter 965. The system 900 may receive traffic information in the intersection area using a receiver interface 980 connected to a receiver 985 through a network 990. Additionally or alternatively, the control system 900 includes a control interface 970 configured to send commands to the devices to change their state. The control interface 970 may use a transmitter 965 to transmit commands and/or any other communication means.

In some implementations, a human interface 910 within the system 900 connects the system to a keyboard 911 and a pointing device 912, where the pointing device 912 may include a mouse, trackball, touchpad, joystick, pointing stick, stylus or touch screen, or the like. The system 900 may also be linked by a bus 906 to a display interface suitable for connecting the system 900 to a display device such as a computer monitor, camera, television, projector, or mobile device. The system 900 may also be connected to an application interface suitable for connecting the system to devices for performing various power distribution tasks.

The system 900 includes a processor 920 configured to execute stored instructions, and a memory 940 storing instructions executable by the processor. Processor 920 may be a single core processor, a multi-core processor, a computing cluster, or any number of other configurations. Memory 940 may include Random Access Memory (RAM), Read Only Memory (ROM), flash memory, or any other suitable memory system. The processor 920 is connected to one or more input and output devices through a bus 906. These instructions implement a method of combined vehicle control.

To this end, the control system 900 includes a traffic configuration 931. For example, the traffic arrangement includes a structure of intersection areas of a first road and a second road. In some embodiments, the structure of the intersection region includes a sequencing region and a control region. The sequencing region includes a segment of the first road and a segment of the second road proximate the intersection, and wherein the control region includes the segment of the first road and the segment of the second road between the intersection and the segment corresponding to the sequencing region. In this manner, the traffic configuration 931 allows the system 900 to control vehicles in different regions differently.

The control system 900 includes a grouping module 933 configured to group vehicles traveling within the ranked area into a series (set) of vehicle groups (groups). After the vehicles are grouped, the grouping module 933 is configured to prevent vehicles of different groups from traveling within the control area at the same time, so that a first vehicle of a subsequent group cannot pass over a last vehicle of a preceding group. For example, the grouping module 933 determines different motion constraints for different groups of vehicles to prevent the different groups of vehicles from traveling within the control area at the same time, ensuring that a first vehicle of a subsequent group cannot pass over a last vehicle of a previous group. For example, if the vehicles in the rank region are grouped into a first group and a second group, wherein the grouping module 933 can determine a minimum speed constraint for the vehicles of the first group and a maximum speed constraint for the vehicles of the second group. In addition, the grouping module 933 can also determine other constraints on vehicle motion, such as lane change constraints, speed constraints, acceleration constraints, and vehicle distance constraints. These additional constraints may further simplify joint control.

The control system 900 includes a trajectory planner 935 that determines the motion trajectories of the same set of vehicles traveling in the control area on the first road and the second road through the intersection. Since the grouping module 933 limits the number of vehicles within the control area, the trajectory planner 935 can determine the trajectory of the vehicles within the control area in real-time. In some embodiments, the trajectory planner 935 determines the motion trajectory in two stages. During the first phase, the trajectory planner 935 solves a mixed integer linear problem to determine optimal times for the vehicles to reach the intersection and speed constraints that ensure that different sets of vehicles arrive at the intersection in sequence. In the second phase, the trajectory planner 935 solves the optimal trajectory problem for determining motion trajectories for different vehicles based on the sequential arrivals at the intersection while also optimizing performance metrics such as energy consumption of the vehicles.

In some implementations, the intersection region includes an information region before the ranking region. In these embodiments, the control system 900 includes a tracker 937 configured to determine a state of a vehicle traveling within the information zone, and track the state of the vehicle within the information zone to estimate a current state of the vehicle within the sequencing zone. In fact, tracking in the information area allows ensuring proper grouping of vehicles.

Fig. 10A shows a schematic diagram of a vehicle 1001 including a controller 1002 in communication with a control system employing the principles of some embodiments. Vehicle 1001, as used herein, may be any type of wheeled vehicle such as a passenger car, bus, or probe car (road). Further, vehicle 1001 may be an autonomous or semi-autonomous vehicle. For example, some embodiments control the motion of vehicle 1001. Examples of motion include lateral motion of the vehicle controlled by a steering system 1003 of the vehicle 1001. In one embodiment, the steering system 1003 is controlled by a controller 1002. Additionally or alternatively, the steering system 1003 may be controlled by the driver of the vehicle 1001.

The vehicle may also include an engine 1006 that may be controlled by controller 1002 or by other components of vehicle 1001. The vehicle may also include one or more sensors 1004 to sense the surrounding environment. Examples of sensors 1004 include a range finder, radar, lidar, and a camera. The vehicle 1001 may also include one or more sensors 1005 to sense its current amount of motion and internal state. Examples of sensors 1005 include Global Positioning Systems (GPS), accelerometers, inertial measurement units, gyroscopes, axis rotation sensors, torque sensors, deflection sensors, pressure sensors, and flow sensors. The sensors provide information to the controller 1002. The vehicle may be equipped with a transceiver 1006 that enables the controller 1002 to have communication capabilities to communicate with the control system of some embodiments over a wired or wireless communication channel. For example, via the transceiver 1006, the controller 1002 receives the motion profile and controls the actuators and/or other controllers of the vehicle according to the received profile.

Fig. 10B shows a schematic diagram of the interaction between the controller 1020 and the controller 1002 of the vehicle 1001, according to some embodiments. For example, in some embodiments, the controller 1020 of the vehicle 1001 is a steering 1025 and brake/throttle controller 1030 that controls the rotation and acceleration of the vehicle 1020. In this case, predictive controller 1002 outputs control inputs to controllers 1025 and 1030 to control the state of the vehicle. The controller 1020 may also include a high level controller, such as a lane keeping assist controller 1035 that further processes control inputs from the predictive controller 1002. In both cases, controller 1020 maps the output of predictive controller 1002 to control at least one actuator of the vehicle, such as a steering wheel, and/or a brake of the vehicle in order to control the motion of the vehicle.

Fig. 10C shows a schematic diagram of an autonomously or semi-autonomously controlled vehicle 1050 for which a dynamically feasible and generally optimal trajectory 1055 may be calculated using some embodiments. The generated trajectory is intended to keep the vehicle within a particular road boundary 1052 and to avoid other uncontrolled vehicles (i.e., obstacles 1051 controlling vehicle 1050). In some embodiments, each obstacle 1051 may be represented by one or more inequality constraints in a temporal or spatial formula that includes a mixed integer optimal control problem for one or more additional discrete variables for each obstacle. For example, based on an implementation configured to implement a hybrid integer model predictive controller, the autonomously or semi-autonomously controlled vehicle 1050 may make discrete decisions in real-time, such as, for example, crossing another vehicle on the left or right side or, alternatively, leaving another vehicle behind in the current lane of road 1052.

In some embodiments, to control the vehicle, the control input includes a command specifying a value of one or a combination of a wheel steering angle and a wheel rotation rate of the vehicle and a measurement including a value of one or a combination of a rotation rate of the vehicle and an acceleration of the vehicle. Each state of the vehicle includes a velocity and a heading angular velocity (heading rate) of the vehicle, such that the motion model associates a value of the control input with a first value of the vehicle state through dynamics of the vehicle at successive time steps, and the measurement model associates the measurement value with a second value of the vehicle state at the same time step.

FIG. 11 illustrates a block diagram of various components that may be used to implement joint control using alternative computers or hardware processors, according to an embodiment. The computer 1111 includes a hardware processor 1140, a computer readable memory 1112, a storage device 1158, and a user interface 1149 having a display 1152 and a keyboard 1151, which are connected via a bus 1156. For example, the user interface 1164, which is in communication with the hardware processor 1140 and the computer-readable memory 1112, obtains and stores signal data examples in the computer-readable memory 1112 upon receiving user input from a surface of the user interface 1164 (the keyboard surface 1164).

The computer 1111 may include a power supply 1154, and the power supply 1154 may optionally be located outside the computer 1111 depending on the application. Linked through bus 1156 may be a user input interface 1157 suitable for connection to a display device 1148, wherein display device 1148 may include a computer monitor, camera, television, projector, mobile device, or the like. The printer interface 1159 may also be connected via the bus 1156 and adapted to connect to a printing device 1132, wherein the printing device 1132 may include a liquid ink jet printer, a solid ink printer, a large commercial printer, a thermal printer, a UV printer, or a dye sublimation printer, among others. A Network Interface Controller (NIC)1134 is adapted to connect to the network 1136 via bus 1156, wherein time series data or other data, etc., may be presented on a third party display device, a third party imaging device, and/or a third party printing device external to the computer 1111.

Still referring to fig. 11, signal data or other data and the like may be transmitted over communication channels of network 1136 and/or stored within storage system 1158 for storage and/or further processing. It is contemplated that the signal data may be initially stored in an external memory and later retrieved by the hardware processor for processing or stored in a memory of the hardware processor to be later processed. The hardware processor memory includes stored executable programs executable by a hardware processor or computer for performing the resiliency recovery system/method, power distribution system operating data, historical power distribution system data of the same type as the power distribution system, and other data related to resiliency recovery of the power distribution system or a similar type of power distribution system to the power distribution system.

Further, signal data or other data may be received wirelessly or hardwired from the receiver 1146 (or external receiver 1138) or transmitted wirelessly or hardwired via the transmitter 1147 (or external transmitter 1139), both the receiver 1146 and the transmitter 1147 connected via the bus 1156. The computer 1111 may be connected to external sensing devices 1144 and external input/output devices 1141 via the input interface 1108. For example, the external sensing device 1144 may include a sensor that collects data before-during-after the collected signal data of the power distribution system. Such as the faulted line segment and fault type caused by the disaster, and the customers affected by the fault. The computer 1111 may be connected to other external computers 1142. Output interface 1109 may be used to output processed data from hardware processor 1140. It should be noted that the user interface 1149, which is in communication with the hardware processor 1140 and the non-transitory computer-readable storage medium 1112, acquires region data and stores it in the non-transitory computer-readable storage medium 1112 upon receiving user input from a surface 1152 of the user interface 1149.

Detailed description of the preferred embodiments

The following description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the following description of the exemplary embodiments will provide those skilled in the art with a description for enabling the implementation of one or more exemplary embodiments. Various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosed subject matter as set forth in the appended claims.

In the following description, specific details are given to provide a thorough understanding of the embodiments. However, it will be understood by those of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, systems, processes, and other elements of the disclosed subject matter may be shown in block diagram form as components in order not to obscure the implementations in unnecessary detail. In other instances, well-known processes, structures and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments. Moreover, like reference numbers and designations in the various drawings indicate like elements.

Furthermore, separate embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be rearranged. A process may terminate when its operations are completed, but may have additional steps not discussed or included in the figure. Moreover, not all operations in any specifically described process may be present in all embodiments. A process may correspond to a method, a function, a step, a subroutine, etc. When a procedure corresponds to a function, the termination of the function may correspond to the return of the function to the calling function or the main function.

Moreover, embodiments of the disclosed subject matter can be implemented, at least in part, manually or automatically. Manual or automated implementations may be performed or at least assisted by the use of machines, hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine-readable medium. The processor may perform the necessary tasks.

The various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments.

Embodiments of the present disclosure may be implemented as a method, examples of which have been provided. The actions performed as part of the method may be ordered in any suitable way. Thus, even though shown as sequential acts in illustrative embodiments, embodiments may be constructed in which acts are performed in an order different than shown, which may include performing some acts simultaneously. Furthermore, use of ordinal terms such as "first," "second," in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements.

Although the present disclosure has been described with reference to certain preferred embodiments, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the present disclosure. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the disclosure.

Claims (20)

1. A system for jointly controlling a vehicle, the system comprising:

a memory configured to store a traffic configuration of an intersection area of a first road and a second road, wherein the intersection area includes a sequencing area and a control area, wherein the sequencing area includes a segment of the first road and a segment of the second road near an intersection, and wherein the control area includes a segment of the first road and a segment of the second road between the intersection and a corresponding segment of the sequencing area;

a processor configured to:

grouping vehicles traveling on the first road and the second road within the sorted area into a series of vehicle groups;

preventing vehicles of different groups from traveling into the control area simultaneously such that a first vehicle of a subsequent group cannot pass over a last vehicle of a preceding group;

determining a movement locus of the same group of vehicles traveling within the control area on the first road and the second road through the intersection; and

and sending the motion trail to a corresponding vehicle.

2. The system of claim 1, further comprising:

an input interface configured to accept traffic information in the intersection region, wherein the processor divides the intersection region into the sequencing region and the control region based on the traffic information.

3. The system of claim 1, further comprising:

an input interface configured to accept traffic information in the intersection region, wherein the intersection region includes an information region preceding the sequencing region, wherein the processor is configured to

Determining a state of a vehicle traveling within the information area;

tracking a state of the vehicle within the information zone to estimate a current state of the vehicle within the sequencing zone; and

grouping the vehicles based on the current state of the vehicles in the rank region.

4. The system of claim 1, wherein the processor determines different motion constraints for different groups of vehicles to prevent the different groups of vehicles from traveling simultaneously into the control area, ensuring that the first vehicle of the subsequent group cannot pass over the last vehicle of the previous group.

5. The system of claim 4, wherein the constraints on motion comprise one or a combination of lane change constraints, speed constraints, acceleration constraints, and vehicle distance constraints.

6. The system of claim 4, wherein the vehicles in the rank region are grouped into a first group and a second group, wherein the processor determines a minimum speed constraint for the vehicles of the first group and a maximum speed constraint for the vehicles of the second group.

7. The system of claim 1, wherein the processor assigns the vehicles in the sequencing zone into different groups based on the time of arrival of a vehicle at the control zone and based on a maximum number of vehicles allowed in the control zone.

8. The system of claim 7, wherein the processor solves a mixed integer linear problem to determine an optimal time for the vehicle to reach the intersection and a speed constraint that ensures that different sets of vehicles reach the intersection in sequence.

9. The system of claim 1, wherein the system is disposed on an edge device disposed proximate the intersection.

10. The system of claim 9, wherein the edge device comprises:

a set of sensors for collecting traffic information in the intersection area, wherein the processor of the system is configured to perform grouping and control based on the traffic information, an

A transmitter to transmit corresponding trajectories to vehicles in the control area.

11. A method for jointly controlling a vehicle, wherein the method uses a processor coupled to a memory storing a traffic configuration for an intersection area of a first road and a second road, wherein the intersection area comprises a sequencing area and a control area, wherein the sequencing area comprises segments of the first road and segments of the second road near an intersection, and wherein the control area comprises segments of the first road and segments of the second road between the intersection and corresponding segments of the sequencing area, wherein the processor is coupled to stored instructions that implement the method, wherein the instructions, when executed by the processor, perform the steps of the method, the method comprising the steps of:

grouping vehicles traveling on the first road and the second road within the sorted area into a series of vehicle groups;

preventing vehicles of different groups from traveling into the control area simultaneously such that a first vehicle of a subsequent group cannot pass over a last vehicle of a preceding group;

determining a movement locus of the same group of vehicles traveling within the control area on the first road and the second road through the intersection; and

causing vehicles in the control area to follow corresponding trajectories.

12. The method of claim 11, wherein the step of preventing different groups of vehicles from simultaneously traveling into the control area comprises the step of applying different motion constraints to different groups of vehicles.

13. The method of claim 12, wherein vehicles in the rank region of the first road are divided into a first group and a second group, wherein the step of preventing comprises applying a minimum speed constraint on vehicles of the first group and applying a maximum speed constraint on vehicles of the second group.

14. The method of claim 12, wherein the constraints on motion comprise one or a combination of lane change constraints, acceleration constraints, and proximity constraints.

15. The method of claim 11, wherein the step of grouping comprises the steps of:

grouping vehicles on the first road based on current states of vehicles on the first road and the second road.

16. The method of claim 15, further comprising the steps of:

determining a status of a vehicle within the information area prior to the rank area; and

tracking a state of the vehicle within the information zone to estimate a current state of the vehicle within the sequencing zone.

17. The method of claim 15, wherein the step of grouping comprises the steps of:

determining a corresponding time at which the vehicle enters the control area based on the current state of the vehicle on the first road and the second road; and

the vehicles are grouped into a series of groups based on their corresponding entry times.

18. The method of claim 15, wherein the step of grouping comprises the steps of:

the vehicles are assigned to different groups based on the proximity of each vehicle to the control area and based on the maximum number of vehicles allowed within the control area.

19. The method of claim 11, wherein the steps of the method are performed by the processor of an edge device installed proximate the intersection.

20. A non-transitory computer readable storage medium having embodied thereon a program executable by a processor to perform a method comprising the steps of:

accessing a traffic configuration for an intersection area of a first road and a second road, wherein the intersection area comprises a sequencing area and a control area, wherein the sequencing area comprises segments of the first road and segments of the second road proximate to the intersection, and wherein the control area comprises segments of the first road and segments of the second road between the intersection and corresponding segments of the sequencing area;

grouping vehicles traveling on the first road and the second road within the sorted area into a series of vehicle groups;

preventing vehicles of different groups from traveling into the control area simultaneously such that a first vehicle of a subsequent group cannot pass over a last vehicle of a preceding group;

determining a movement locus of the same group of vehicles traveling within the control area on the first road and the second road through the intersection; and

causing vehicles within the control area to follow corresponding trajectories.

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